Image sharpening is performed to improve the appearance of digital images and particularly the legibility of documents. Most filters that perform image sharpening use variations of unsharp masking, a linear sharpening filter, which generates overshoots and undershoots at abrupt edges. Unsharp masking tends to produce visually favorable results for natural images, but not for document images. In images of documents, the unsharp masking can create overshoot artifacts, which reduce image compressibility of text-rich images.
An alternative to unsharp masking is based on the mathematical morphology approach, which is frequently reduced to combinations of neighborhood-minimum and neighborhood-maximum filters. Morphological filters that combine smoothing and sharpening, such as the Mean of Least Variance (MLV) filter and variants of toggle mapping filter, tend to strongly posterize images, i.e. reduce original images to piecewise constant intensity functions. While this effect can be desirable for purely textual images or medical images, it does not yield visually acceptable results for compound document images, which may also contain photos, variable backgrounds, and other image regions that do not correspond to piecewise constant intensity profiles.